Multi-modal medical image registration based on non-rigid transformations and feature point extraction by using wavelets

In order to correctly match two sets of images from different modalities, our method applies a non-rigid transformation to one set to get as close as possible to the other. This requires the estimation of the optimal similarity transformation between the two set of images. Estimation of the non-rigid deformation between the two sets of images is referred to as the deformation estimation between the pair of three-dimensional object extracted from both sets. We present a new methodology for image registration by first extracting objects from the set of images by reconstructing the object surfaces where this extraction supports semi-automatic segmentation of sets of 3-D medical images and then finding the best similarity transformation based on matching of two sets of surface points, but also incorporates the matching of two sets of feature points, and we have shown that deformation estimates based on simultaneous matching of surfaces and features are more accurate than those based on surface matching alone. This is especially true when the deformation involves physically realistic cases, such as those in human organs. Our technique uses free-form deformation models and applies the wavelet transform to extract feature points in the 3-D space. Feature point extraction also provides a means to compute the error in our estimates. We have applied our method to register sequences of MRI images to histology images of the carotid artery.